At EuroShop 2026 in Düsseldorf, Datalogic is presenting a new generation of retail systems in which artificial intelligence is integrated directly into scanners and handheld devices. The company positions this “embedded AI” approach as a way to reduce shrinkage, streamline checkout processes, and improve inventory management without relying on external cameras or cloud-based processing.
Datalogic’s presentation reflects the growing pressure on retailers from rising operational costs, tighter margins, and increasing customer expectations for fast and frictionless shopping. By embedding intelligence directly into everyday devices, the company aims to automate supervision and control tasks that previously required manual intervention.
AI Inside the Checkout Scanner
A central element of the presentation is the company’s latest Magellan fixed retail scanners, which combine cameras and AI processing in a single unit. These systems use computer vision models to analyze products as they pass through self-checkout or assisted checkout lanes.
The embedded software monitors item movement and appearance in real time to detect situations such as products passing without being scanned, multiple items being registered as one, mismatched labels, or incorrect recognition of fresh produce. Because this analysis takes place inside the scanner, decisions are made immediately, without sending images or data to external servers. According to Datalogic, this reduces response times, lowers infrastructure requirements, and simplifies installation.
Combining Vision, RFID, and Barcodes
Datalogic is also demonstrating systems that integrate barcode scanning, RFID reading, and visual recognition. By correlating these data sources, the AI software verifies whether the physical object, barcode, and RFID tag correspond to the same product.
This multi-sensor approach is intended to improve visibility of item movement at critical points in the store, including checkout lanes and exits. The company says this supports more accurate inventory tracking and strengthens loss prevention measures without adding separate monitoring systems.
Self-Shopping Devices With Automated Validation
For self-scanning customers, Datalogic is presenting AI-enabled handheld devices equipped with rear-facing cameras. These devices continuously compare scanned products with the contents of the shopper’s basket.
When an item appears in the basket without being registered, the system flags the discrepancy for review by store staff. According to the company, this reduces the need for random audits, lowers the workload for employees, and minimizes disruption to the shopping experience.
AI for Inventory and Store Operations
Beyond checkout and self-scanning, Datalogic is applying AI to in-store operations such as stock counting, shelf replenishment, and order picking. Mobile computers in the Memor and Skorpio families collect operational data and use pattern-recognition models to identify recurring issues, including frequent out-of-stocks or picking errors.
Based on these patterns, the systems can generate automated alerts and task recommendations. The aim is to improve shelf availability, reduce manual reporting, and increase overall productivity on the shop floor.
Smarter Barcode Reading
In its new Gryphon handheld scanners, the company is using neural network models for barcode decoding. Instead of relying solely on predefined rules, the AI reconstructs damaged, blurred, or poorly lit codes.
Datalogic states that this approach improves reading accuracy, reduces the number of repeated scans, and lowers energy consumption. The company presents this as part of its broader effort to combine performance improvements with sustainability objectives.
Predictive Maintenance Through Cloud Analytics
All connected devices can be managed through Datalogic Connect, the company’s cloud platform. Within this system, AI models analyze performance data, error logs, and usage patterns to identify signals that may indicate upcoming failures. This enables predictive maintenance, allowing retailers to intervene before devices stop working. According to Datalogic, this can reduce downtime and improve reliability across large store networks.
